3. Climate models -- the most sophisticated models of natural phenomena. Still, the range of uncertainty in responses to CO 2 doubling is not decreasing. Can this be a matter of intrinsic sensitivity to model parameters and parameterizations, similar to but distinct from sensitivity to initial data? Dynamical systems theory has, so far, interpreted model robustness in terms of structural stability; it turns out that this property is not generic. We explore the structurally unstable behavior of a toy model of ENSO variability, the interplay between forcing and internal variability, as well as spontaneous changes in mean and extremes.

4. Differential Delay Equations (DDE) offer an effective modeling language as they combine simplicity of formulation with rich behavior… To gain some intuition, compare The only solution is The general solution is given by In particular, oscillatory solutions do exist. ODEDDE i.e., exponential growth (or decay, for  < 0)

5. Battisti & Hirst (1989) SST averaged over eastern equatorial Pacific Bjerknes’s positive feedback Negative feedback due to oceanic waves The model reproduces some of the main features of a fully nonlinear coupled atmosphere-ocean model of ENSO dynamics in the tropics (Battisti, 1988; Zebiak and Cane, 1987).

6. Battisti & Hirst (1989) Suarez & Schopf (1988), Battisti & Hirst (1989) Cubic nonlinearity The models are successful in explaining the periodic nature of ENSO events. But they… have a well defined period, which is not the case in observations can’t explain phase locking predict a wrong ENSO period (1.5-2 years)

7. Battisti & Hirst (1989) Suarez & Schopf (1988), Battisti & Hirst (1989) Tziperman et al., (1994) Seasonal forcingRealistic atmosphere-ocean coupling (Munnich et al., 1991)

8. Thermocline depth deviations from the annual mean in the eastern Pacific Wind-forced ocean waves (E’ward Kelvin, W’ward Rossby) Delay due to finite wave velocity Seasonal-cycle forcing Strength of the atmosphere-ocean coupling

10. “High-h” season with period of about 4 yr; notice the random heights of high seasons Rough equivalent of El Niño in this toy model (little upwelling near coast)

11. “Low-h” (cold) seasons in successive years have a period of about 5 yr in this model run. Negative h corresponds to NH (boreal) winter (upwelling season, DJF, in the eastern Tropical Pacific)

12. Interdecadal variability: Spontaneous change of (1)long-term annual mean, and (2)Higher/lower positive and lower/higher negative extremes N.B. Intrinsic, rather than forced!

13. Bursts of intraseasonal oscillations of random amplitude Madden-Julian oscillations, westerly-wind bursts?

14. Theorem Corollary A discontinuity in solution profile indicates existence of an unstable solution that separates attractor basins of two stable ones.

16. Trajectory maximum (after transient):  Smooth map Monotonic in b Periodic in 

17. Trajectory maximum (after transient):  Smooth map No longer monotonic in b, for large  No longer periodic in  for large 

18. Trajectory maximum (after transient):  Neutral curve f (b,  appears, above which instabilities set in. Above this curve, the maxima are no longer monotonic in b or periodic in   and the map “crinkles” (i.e., it becomes “rough”)

19. Trajectory maximum (after transient):  The neutral curve that separates rough from smooth behavior becomes itself crinkled (rough, fractal?). The neutral curve moves to higher seasonal forcing b and lower delays . M. Ghil & I. Zaliapin, UCLA Working Meeting, August 21, 2007

20. This region expanded

21. M. Ghil & I. Zaliapin, UCLA Working Meeting, August 21, 2007

22. Instability point

25. Delay,  b  Maxima Minima

26. Shape of forcing Maxima Minima Time

28. b = 1,  = 10,  = 0.5 100 initial (constant) data4 distinct solutions

29. Solution profile Initial data (b = 1.4,  = 0.57  = 11) Stable solutions (after transient)

30. (b = 1.0,  = 0.57) (b = 3,  = 0.3) (b = 1.6,  = 1.6) (b = 2.0,  = 1.0) (b = 1.4,  = 0.57)

31. 1.A simple differential-delay equation (DDE) with a single delay reproduces the realistic scenarios documented in other ENSO models, such as nonlinear PDEs and GCMs, as well as in observations. 2.The model illustrates well the role of the distinct parameters: strength of seasonal forcing b, ocean-atmosphere coupling , and delay  (propagation period of oceanic waves across the Tropical Pacific). 3. Spontaneous transitions in mean temperature, as well as in extreme annual values occur, for purely periodic, seasonal forcing. 4.A sharp neutral curve in the (b–  ) plane separates smooth behavior of the period map from “rough” behavior; changes in this neutral curve as  changes are under study. 5.We expect such behavior in much more detailed and realistic models, where it is harder to describe its causes as completely.

33. Solution profile at k =1

34. Solution profile at k =3

35. Solution profile at k =3.5

36. Solution profile at k =4

37. Solution profile at k =50

38. Solution profile at k =100

39. Solution profile at k =1000

40. Constant history H, b =1.0,  =0.5  =1.0  =3.5  =4.0  =50

41. Constant history H,  =11, b =1.4,  =0.57 H=[-1,1] H=[0.5,0.51] H=[0.5,0.5001]